Xiao Sun, Ymyang, Xinyi Jiang, Yu Tian, Junnan Zhu, Jiang Zhong, Qin Lei, Jingwang Huang, Haoyang Zeng, Xinyu Zhou, Xin Xiao, Kaiwen Wei. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Ymyang, Jiang Zhong, Li Jin, Xiao Sun, Jingwang Huang, Gaojinpeng, Qing Liu, Yang Bai, Jingyuan Zhang, Rui Jiang, Qin Lei, Kaiwen Wei. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

In existing secret medical-image sharing (SMIS) schemes, to protect and manage secret medical-images (SMIs), the sharing and recovery of each SMI are implemented by local servers of medical institutions. However, since a lot of SMIs are produced by personal smart terminal devices in Internet of Things (IoT) environment, directly implementing the sharing and recovery processes will cause excessive communication and computing burden for those local servers, which makes the existing SMIS schemes not suitable for IoT environment. To address the above issue, we propose an encrypted domain SMIS (Enc-SMIS) scheme with secure outsourcing computation for protecting and managing medical images in IoT environment. In the proposed scheme, the medical images are first encrypted using fully homomorphic encryption (FHE) and then outsourced to a cloud server for generating a set of image shares. Subsequently, these shares are stored separately in different local servers of medical institutions. Furthermore, the recovery process is also outsourced to the cloud server when doctors need to observe the patients’ medical images. Compared with the existing SMIS schemes, the proposed Enc-SMIS scheme alleviates the computing and communication burden on local servers significantly with secure outsourcing computation in the semi-honest model, and thus supports the storage and management of medical images well in IoT environment.

The purposes are to investigate how blockchain can solve the security problems in Intelligent Autonomous Transport System (IATS) and intelligentize the logistics transportation development. Regarding the scarcity of trust and concentration of rights caused by the centralized structure of traditional logistics information systems, a blockchain-based IATS is proposed. The system employs Ethereum as the underlying blockchain to record sensitive information, such as system orders, cargos, and personnel information on the blockchain, ensuring the non-tampering and credibility of data. Simultaneously, an order management module, a warehouse management module, a transportation management module, a transaction management module, and a system management module are established. In the meantime, the Light Gradient Boosting Machine (LightGBM) algorithm is utilized to recommend vehicle and cargo matching during transportation. Finally, the constructed algorithm model is simulated to analyze its performance. Results demonstrate that the security prediction accuracy of the proposed algorithm reaches 88.72%; moreover, the security prediction precision, recall, and F1 of the proposed algorithm are considerably better than those of other algorithms. Furthermore, the actual effect of each algorithm is analyzed. The LightGBM algorithm outperforms other algorithms and unused algorithms in click rate, conversion rate, turnover rate, and average response time. Therefore, the constructed blockchain-based IATS has excellent security performance and prediction accuracy, which provides an experimental basis for the later intelligent logistics transportation development.

With the explosive growth of data generated by the Internet of Things (IoT) devices, the traditional cloud computing model by transferring all data to the cloud for processing has gradually failed to meet the real-time requirement of IoT services due to high network latency. Edge computing (EC) as a new computing paradigm shifts the data processing from the cloud to the edge nodes (ENs), greatly improving the Quality of Service (QoS) for those IoT applications with low-latency requirements. However, compared to other endpoint devices such as smartphones or computers, distributed ENs are more vulnerable to attacks for restricted computing resources and storage. In the context that security and privacy preservation have become urgent issues for EC, great progress in artificial intelligence (AI) opens many possible windows to address the security challenges. The powerful learning ability of AI enables the system to identify malicious attacks more accurately and efficiently. Meanwhile, to a certain extent, transferring model parameters instead of raw data avoids privacy leakage. In this paper, a comprehensive survey of the contribution of AI to the IoT security in EC is presented. First, the research status and some basic definitions are introduced. Next, the IoT service framework with EC is discussed. The survey of privacy preservation and blockchain for edge-enabled IoT services with AI is then presented. In the end, the open issues and challenges on the application of AI in IoT services based on EC are discussed.